Bees in My Bonnet

by johnmccreery

Picking up where I left off in my reading of Miller and Page Complex Adaptive Systems. They write (p. 14),

Heterogeneity is often a key driving force in social worlds. In the Standing Ovation Problem, the heterogeneity that arose from where people sat and with whom they associated resulted in a model rich in behavioral possibilities. If heterogeneity is a key feature of complex systems, then traditional social science tools — with their emphasis on average behavior being representative of the whole—may be incomplete or even misleading.

Beehives provide two examples.

In the first, the heterogeneity is diversity in the temperature at which individual bees feel hot or cold. When bees feel hot, they spread out and fan their wings to cool the hive. When they feel cold, they crowd together and buzz their wings rapidly to generate heat. Note, however, the effect of heterogeneity. As the first bees to feel hot or cold start to behave as described above, the result of their behavior is to move the temperature of the hive away from the thresholds at which other bees will join them. Start cooling the hive, and those who feel cool enough won’t join in. Start heating the hive, and those who feel warm enough won’t join in. In this case, negative feedback drives the system toward equilibrium.

In the second case, sentinel bees guarding the hive respond to a threat by attacking it. As they become excited they stimulate other bees who join them, stimulating still more bees that stimulate still more bees until all of the bees are attacking the threat. In this case, positive feedback drives a process that drives the system away from equilibrium — a pattern familiar in other species as well, in mobbing, stampedes, pogroms and financial panics. If the threat goes away quickly enough, the hive may settle down. If it persists, the hive goes crazy and stays that way.

Sound familiar?


7 Comments to “Bees in My Bonnet”

  1. bzzzztt! Well, surely we are a bit more complicated than bees. We have social institutions that can dampen or encourage any feedback loops that might be out there. So I guess this would add an extra layer to it. I would guess that most of the time our social conventions put a damper on things. On the other hand when people are stressed they tend to lose faith in the police or the government so that might lessen any dampening effect and things might spin out of control more quickly.

  2. Right. But I have the feeling that you and Asher are the smart-ass kids in the class, who already know where this is going. Think of all of the years in which kids have been graduating with economics degrees, thinking that markets tend to equilibrium because the models they learn all focus on averages and negative feedback. Think of all of the shareholders who got burned by following the advice of financial planners with the same kind of training. This stuff about heterogeneity, positive feedback, bubbles and panics should be taught in high school.

  3. “We have social institutions that can dampen or encourage any feedback loops that might be out there.”

    Yes. This was Karl Polanyi’s thesis in The Great Transformation – that as the horrors of a pure free market became evident in the early 19th century, society moved to protect itself by limiting market freedom and buffering its worst effects.

    “This stuff about heterogeneity, positive feedback, bubbles and panics should be taught in high school.”

    Oh yes. Yes, yes.

    “Sound familiar?”

    Yes again. Great post.

  4. This sounds like a really fun book, John. I wouldn’t say I see where it’s going, but the contrast between “traditional social science tools” and agent-based simulations is where I was heading in the last post. I don’t think it’s just social science. Even physical sciences attempt to use equations to describe the “average” behavior of whole systems. It works great for certain types of systems and fails miserably for others. Reflexivity is a key indicator. Heterogeneity is another, but for more involved reasons (that is, if you observe a system in mid-operation and you see heterogeneity, you might conclude that the system “supports” heterogeneity. Chaotic and static systems each tend toward their own sort of uniformity).

  5. Couldn’t agree more. One of the things I really love about this stuff is the way it goes slashing right across old disciplinary boundaries between the physical and social sciences. Modeling agents instead of pushing them off into a separate world of spirit, culture, etc., feels so right to me.

  6. I’ve been reading the superb Hollow Land: Israel’s Architecture of Occupation (2007) by Eyal Weizman. His discussion of the archictonics of urban warfare build on the framework and even the metaphors invoked in this series of posts. Weizman cite’s Shimon Naveh, former brigadier general in the Israeli army and co-director of Operational Theory Research Institute, a military think tank:

    “Although so much is invested in intelligence, fighting in the city is still incalculable and messy. Violence makes events unpredictable and prone to chance. Battles cannot be scripted. Decisions to act must be based on chance, probability, contingency and opportunity, and these must be taken on the ground and only in real time.”

    Weizman says that military intelligence regards urban warfare as “the ultimate post-modern form of warfare”. He goes on:

    “According to Naveh, a central category in the Israeli Defense Force conception of the new urban operations is ‘swarming’ — a term that has, in fact, been part of US military theory for several decades… Swarming seeks to describe military operations as a network of diffused multiplicity of small, semi-independent but coordinated units operating in general synergy with all others… The term is in fact derived from the Artificial Intelligence principle of ‘swarm intelligence. This principle assumes that problem-solving capacities are found in the interaction and communication of relatively unsophisticated agents (ants, birds, bees, soldiers) without (or with minimal) centralized control. ‘Swarm intelligence’ thus refers to the overall, combined intelligence of a system, rather than the intelligence of its constituent parts. A swarm ‘learns’ through the interaction of its constitutive elements, through their adaptation to emergent situations, and in reaction to changing environments.”

  7. I seem to remember that the Germans pioneered this swarming approach early in the second great war, to which is attributed their superiority over the formally impressive but ponderously overplanned and overcentralized French. German ground commanders were given an overview of the operation and objectives and cued on their part of it, but then allowed to read and react to situations as they developed. The French in contrast required operational feedback to go all the way up the command hierarchy and decisionmaking to go all the way back down. As a result they lurched around helplessly, always responding several stimuli too late.

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